The goal of the Methods in Computational Neuroscience Course at the Marine Biological Laboratory is to train 22 advanced students and post-doctoral fellows a year in the fundamental intellectual issues in computational neuroscience and to avail them with analytical and numerical tools to assist in their studies. The course has unique features that would be hard if not impossible to replicate in a university setting. First, it continues to attract and educate students from a broad range of established disciplines that impact upon neuroscience, including biology, cognitive science, computer science, engineering, mathematics and physics. Secondly, as a means to provide a coherent and relatively complete perspective on the growing field of computational neuroscience, students are exposed to a large visiting faculty in addition to receiving instruction and guidance from directors, scholars-in-residence and course assistants. It is unlikely that such a combination of faculty could be convened for an analogous course in a university setting. Lastly, the course provides students with access to state-of-the-art computational techniques and computer hardware fundamental to simulating neural systems from the detailed cellular through more abstracts systems level. The course is organized as an intensive four week lecture and laboratory series. The lectures progress along an increasing scale of computational complexity. They initially focus on the electrophysiology and chemical dynamics of individual neurons and synapses. This is followed by lectures on the dynamics and plasticity of local neuronal circuits and small nervous systems, topics that illustrate how single-cell properties shape computations by small networks. A final series of lectures addresses computations that involve sensory and perceptual issues in large neuronal systems, particularly cortex, and the emerging application of abstract models to provide a framework for understand these systems. The laboratory emphasizes the spectrum of tools necessary for successful research in computational neuroscience. On the one hand, the course provides training with general purpose simulation software as well as with simulation software that is specific to single cell dynamics, or to both single cells and large networks. On the other hand, students are trained in areas of applied mathematics, such as probability theory and matrix algebra, that are essential to formulating and solving problems in the field.